Course Information

Estimation Theory - Fall 2019
Instructor: Prof. Songhwai Oh (오성회)
Email: songhwai (at) snu.ac.kr
Office Hours: Friday 2:00-4:00PM
Office: Building 133 Room 405
Course Number: 430.714
Time: MW 2:00-3:15 PM
Location: Building 302 Room 408
TA: Chanho Ahn (안찬호)
Email: chanho.ahn (at) rllab.snu.ac.kr
Office: Building 133 Room 610
 

Course Description

This course introduces classical and modern topics in estimation theory to graduate level students. Topics include minimum variance unbiased estimators, the Cramer-Rao bound, linear models, sufficient statistics, best linear unbiased estimators, maximum likelihood estimators, least squares, exponential family, multivariate Gaussian distribution, Bayes risk, minimum mean square error (MMSE), maximum a posteriori (MAP), linear MMSE, sequential linear MMSE, Bayesian filtering, Kalman filters, extended Kalman filter, unscented Kalman filter, particle filter, data association, multi-target tracking, and Gaussian process regression. Lectures will be in English.

Announcements

  • [11/20] The final exam will be held in class on 12/4 (Wed). The exam is closed-book but you can bring one sheet (A4) of hand-written notes on both sides. You have to turn in this cheat sheet with your exam.
  • [10/14] The midterm will be held in class on 10/23 (Wed). The exam is closed-book but you can bring one sheet (A4) of hand-written notes on a single side (the other side must be blank). You have to turn in this cheat sheet with your exam. Previous midterms: 2018, 2017.
  • [08/26] Please read Ethics of Learning.

Schedule

Week Reading Date Lecture Date Lecture Assignment
1 Kay Ch. 1
Simon Ch. 1, 2
9/2 9/4  
2


Kay Ch. 2, Ch. 3.1 - 3.9, Ch. 4

9/9 9/11

HW 1 (due: 9/18, in class)

Kay 3.1, 3.2, 3.4, 3.9, 4.1, 4.5

3 Kay Ch. 5, Ch. 6

9/16 9/18  
4 Kay Ch. 7.1 - 7.6, Ch. 8 9/23 9/25

HW 2 (due: 10/2, in class)

Kay 5.3, 5.6, 5.9, 6.1, 6.2, 7.1

5 Kay Ch. 10, Ch. 11, Ch. 12 9/30 10/2  
6 Kay Ch. 12 10/7 10/9
  • Holiday

HW 3 (due: 10/14, in class)

Kay 8.6, 8.12, 10.10, 10.12, 11.3

7 Simon Ch. 5 10/14 10/16  
8 Simon Ch. 6 10/21 10/23
  • Midterm
    • in class
 
9 Simon Ch. 7 10/28 10/30
  • No class
 
10   11/4
  • No class
11/6
  • No class
 
11 Simon Ch. 9 11/11 11/13

HW 4 (due: 11/20, in class)

Simon 5.2, 5.5, 5.7, 6.1, 6.12, 7.4

12 Simon Ch. 13, Ch. 14 11/18 11/20  
13 Simon Ch. 15 11/25 11/27  
14   12/2   12/4
  •  Final Exam
    • Locaition: 302-408
    • Time: 12:30 - 3:25
 

Textbooks

  • [Recommended] Steven M. Kay, "Fundamentals of Statistical Signal Processing, Volume I: Estimation Theory", Prentice Hall, 1993.
  • [Recommended] Dan Simon, "Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches", Wiley-Interscience, 2006.

Prerequisites

  • Students must have a solid background in linear algebra, linear system theory, and probability.

Topics

  • Introduction and review of probability and linear system theory
  • Minimum variance unbiased estimators
  • Cramer-Rao lower bound
  • Linear models and sufficient statistics
  • Best linear unbiased estimators and maximum likelihood estimators
  • Least squares, exponential family, and Bayesian approaches
  • Multivariate Gaussian distribution
  • Bayes risk, minimum mean square error (MMSE), and maximum a posteriori (MAP)
  • Linear MMSE and sequential linear MMSE
  • Bayesian filtering
  • Kalman filtering
  • Advanced topics in Kalman filtering
  • Extended Kalman filter, unscented Kalman filter, and particle filter
  • *Data association and multi-target tracking
  • *Gaussian process regression (*if time permits)